Search Results (1,158)

In this work, we present a decentralized, event-driven control architecture for collaborative rigid object manipulation using omnidirectional wheeled mobile robots. Unlike fixed manipulators, mobile manipulation requires complex coordination between robots, making robustness and fault tolerance critical. Our framework is implemented in ROS2, in which each robot operates independently, with control, kinematic, and motor nodes that communicate via structured message passing. This decentralized design enhances fault tolerance, as individual component failures do not compromise the entire system. To enable perception, an ArUco-based vision system is employed to estimate robot and object poses, supporting the execution of three coordinated subtasks: approaching, grasping, and transporting. The proposed scheme is validated in a Gazebo simulation through different experiments, in which two robots successfully manipulate individual cubes or a beam. Results demonstrate that the proposed event-driven, decentralized control strategy enables consistent coordination, fault-tolerant operation under agent failures, and successful task execution in collaborative manipulation scenarios. Full article

Aiming at the problems that existing detection methods struggle to accurately identify early insulation faults of induction motors, are susceptible to interference, and have poor installation adaptability, a non-intrusive detection method for early insulation faults of induction motors based on a microstrip antenna array is proposed. Relying on the low-loss electromagnetic wave transmission characteristic of the heat dissipation hole at the tail of the induction motor, a four-element microstrip antenna array with multiple narrow beams and dual detection frequencies is designed, with the detection frequencies accurately set at 1.14 GHz and 2.23 GHz, which effectively avoids the motor operation noise frequency band (≤300 MHz) and the strong interference frequency band of mobile base stations (900 MHz, 1.8 GHz, 2.4 GHz). Utilizing the high gain and strong directivity of the array antenna, the accurate extraction and amplification of weak electromagnetic wave signals from early insulation fault discharge penetrating through the heat dissipation hole are realized. The full-dimensional simulation design of the antenna array is completed by using HFSS electromagnetic simulation software, and an industrial-grade experimental platform is built to carry out multi-condition verification experiments. The results show that the proposed detection system can realize non-intrusive, non-stop, and non-disassembly identification of early insulation discharge faults in induction motors, with a fault recognition rate of 94% for single faults and 90% for composite faults, and the average signal-to-noise ratio reaches 31.6–35.2 dB. Even under strong industrial electromagnetic interference, the recognition rate remains above 85%. This method overcomes the problems of traditional methods such as severe noise interference, difficult installation, and inability to monitor online, providing a high-efficiency scheme for real-time insulation state monitoring of industrial induction motors with good engineering application value. Full article

Accurate demagnetization fault diagnosis is critical to ensuring the safety and reliability of permanent magnet synchronous motors (PMSMs). However, the number, location, and severity of demagnetized permanent magnets are mutually coupled, leading to a combinatorial explosion of fault patterns. Existing methods are largely limited to idealized assumptions involving single-magnet demagnetization or uniform demagnetization of multiple magnets, making it difficult to characterize the random nature of demagnetization in practical operation. Thus, this paper proposes a precise demagnetization fault diagnosis method based on a novel search coil (SC) configuration, in which only two toroidal-yoke-type search coils are installed in the stator slots. The proposed method partitions the rotor permanent magnets into several modules and categorizes the infinite demagnetization fault patterns into 26 representative patterns, effectively addressing the issue of fault mode explosion. Theoretical analysis and experimental results show that the voltage waveforms of the search coil over a single electrical period exhibit significant and stable differences across the identified patterns. By constructing feature vectors based on these differences, a physically interpretable mapping between the feature vectors and fault patterns is established. Combined with a corresponding pattern recognition algorithm, the proposed method enables fast and accurate differentiation of the 26 patterns without the need for complex machine learning models, thereby achieving precise localization of demagnetized permanent magnets. Simulation and experimental results verify the correctness and effectiveness of the proposed method. Full article

Substantial progress has been made in bearing-fault classification under same-condition settings, yet cross-condition diagnosis remains affected by three coupled issues: speed-induced temporal-scale perturbation, insufficient use of structured label information, and domain shift across operating conditions. To address these issues, this paper presents BearingPRO, a unified framework that combines physics-guided representation alignment with group ordinal labeling for bearing-fault classification. The framework contains three modules. First, a TimeWarp module applies controlled temporal stretching and compression to emulate waveform variations induced by speed changes. Second, a Grouped Ordinal module introduces intra-group ordinal constraints according to the hierarchical relation between fault type and fault severity. Third, a Physics-Guided Representation Alignment (PGRA) module uses rotational-speed priors for carrier-frequency calibration, envelope extraction, and cross-domain alignment. On the CWRU bearing dataset, under the 0 HP → 3 HP transfer task, BearingPRO achieves 0.9205 ± 0.0088 accuracy and 0.8962 ± 0.0105 Macro-F1 in the unified reproduction setting used in this study. Relative to the re-implemented comparison methods under the same backbone and training budget, the proposed framework yields higher mean performance and lower variance. Ablation results further indicate that temporal-scale modeling, grouped ordinal supervision, and physics-guided alignment play complementary roles in the current setting. At the same time, the scope of the conclusion is explicitly bounded: the present evidence is obtained on the CWRU test rig, within the considered speed range, and under artificially introduced point defects; therefore, the method is presented as a well-supported cross-condition classifier for this benchmark, not as a universally validated solution for all motors. Full article

Low-voltage (LV) alternating current (AC) power distribution systems are widely used, where phase-to-neutral short-circuit faults are a major cause of electrically induced fires. Prior to a circuit breaker interruption, arc discharges may develop between conductors, leading to intense localized heating of the cable insulation and a potential ignition risk. In this study, a magnetohydrodynamic (MHD) model of 220 V AC short-circuit arcs is established to investigate the coupled electrical and thermal behavior of arc discharges and their induced heating effects on conductor insulation. The transient temperature distribution in the arc region and insulation layer is numerically analyzed under different tripping currents and tripping times, and insulation ignition risk is evaluated based on characteristic thermal thresholds. To validate the simulations, a controllable 220 V AC short-circuit experimental platform is developed using a motor-driven wire contact mechanism. Circuit breakers rated at 20 A, 32 A, and 63 A are tested, and short-circuit current and voltage waveforms are recorded. The results indicate that insulation ignition risk is jointly governed by short-circuit current magnitude and breaker tripping time. Delayed interruption significantly increases insulation temperature and ignition susceptibility, whereas rapid interruption effectively suppresses arc-induced heating. Full article

Intelligent fault diagnosis of rotating machinery is essential for manufacturing reliability and predictive maintenance, yet deployment of deep learning models is limited by data scarcity: fault samples are rare, costly, and hazardous to obtain. Conventional synthetic data methods such as Generative Adversarial Networks and Variational Autoencoders often exhibit mode collapse, spectral distortion, and limited physical interpretability. This work presents MechaForge, a multi-strategy framework that employs Large Language Models (LLMs) as physics-guided generators for bearing fault time-series data. The approach is grounded in bearing kinematics, Motor Current Signature Analysis (MCSA), and the interpretation of in-context learning as implicit Bayesian inference. Within MechaForge, four progressively constrained tracks are defined: a real-data baseline, few-shot LLM mimicry, multi-stage semantic reasoning, and physics-guided generation with constraints on root mean square, kurtosis, and fault-band spectral energy. For direct benchmarking, conventional VAE- and GAN-based augmentation baselines are additionally evaluated under the same dataset split, synthetic-data budget, downstream CNN architecture, and evaluation metrics. Experiments on the Paderborn bearing dataset show that the Basic LLM track achieves the strongest performance under the present protocol (0.7862 accuracy, 0.7648 macro-F1), exceeding the added VAE and GAN baselines (both 0.7428 accuracy; 0.7202 and 0.7257 macro-F1, respectively), while a control experiment confirms that synthetic data provides discriminative structure rather than labeled noise. These results indicate the promise of LLM-based diagnostic augmentation under data scarcity in the present Paderborn setting, rather than a definitive demonstration of broad transferability across fault-diagnosis scenarios. Full article

Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity and cost. To overcome these limitations, this study proposes a wavelet-based health monitoring structure for detecting electrical and mechanical faults using motor current signal analysis. A dynamic model of the train door actuation mechanism, including a DC motor, gearbox, and lead screw, was developed in MATLAB/Simulink to simulate conditions such as armature electrical faults, brush wear, increased friction, and lead screw misalignment. Motor current signals were analyzed using the Discrete Wavelet Transform with a Daubechies (db10) mother wavelet to extract diagnostic features based on the L1-norms of wavelet coefficients at levels W8 and W9 along with the motor starting current peak. Experimental validation using a LabVIEW-based test platform demonstrated fault detection accuracy above 96% with a response time below 0.3 s, confirming the effectiveness of the proposed approach for predictive maintenance of railway door systems. Full article

As a critical element of the drive motor, rolling bearings are susceptible to localized defects under complex loads and varying operating conditions. Such defects typically generate periodic transient shocks, which reflect bearing fault features. However, the accurate extraction of fault-related transient components becomes challenging due to strong noise influence. To address this issue, a concave sparsity-assisted generalized dispersive mode decomposition (CSA-GDMD) method is developed to enhance fault feature extraction. This method introduces a non-convex sparse model based on generalized mini-max concave (GMC) regularization to preprocess the vibration signal. The GMC penalty effectively suppresses background noise while better preserving the amplitude characteristics of the transient shocks. Subsequently, GDMD is applied to progressively extract transient shock components from the preprocessed signal and reconstruct the signal, resulting in more prominent fault-related transient components. The simulation results show that CSA-GDMD significantly improves the signal-to-noise ratio (SNR), from 6.5905 dB at −15 dB to 9.5122 dB at 5 dB, and reduces the root mean square error (RMSE) from 0.0280 to 0.0196. Consequently, the fault feature frequencies can be identified more clearly in the envelope spectrum, further confirming the accurate fault diagnosis capability of the proposed method for bearing faults under strong noise conditions. Full article

This paper deals with the investigation of the best topology of a five-phase fault-tolerant spoke-type permanent magnet (PM) motor for the propulsion of a multirotor aerial vehicle. This study is carried out through four stages. First, an assessment of the PM configuration effect on motor performance, considering three positions, namely surface PM, spoke-type PM, and V-shape PM. Second, an evaluation of the optimisation formulation problem on motor performance, where three formulations, respectively, involving either electric motor (EM) efficiency, EM efficiency and torque, or EM efficiency and active weight are considered for this purpose. Third, the stator winding configuration effect on performance in healthy and faulty operation mode (OM), e.g., open-circuit fault (OC) and inter-turn short-circuit (ITSC) fault, is also assessed. This evaluation is performed considering two winding configurations, namely fractional slot concentrated winding (FSCW) with single-layer (SL) or dual-layer (DL) winding. Fourth, a modified rotor geometry is proposed, based on the airgap length variation, in order to increase the airgap flux density amplitude and thus improve the motor torque and power densities. A comparative study, in this case, is performed with a classical rotor geometry in order to assess their influence on motor performance in healthy and faulty operation mode (OM). In addition, this paper presents a quantitative comparison of the proposed five-phase motor and a three-phase spoke-type PM motor, where the results, in healthy and faulty OM, show the interest of the proposed multiphase motor. Full article

This paper investigates the reliable stabilization and $L_2-L_{\infty }$ performance control problem for discrete-time descriptor systems described by Takagi–Sugeno (T-S) fuzzy models under stochastic data dropouts and actuator faults. In view of the practical situation that system states are usually unmeasurable, a novel observer-based proportional–derivative (PD) control strategy is proposed. Different from traditional state feedback, the PD structure effectively alleviates the inherent structural constraints of descriptor systems and relaxes the conditions for system regularity and causality. By constructing a parameter-dependent Lyapunov functional and using the Schur complement lemma, sufficient conditions are derived in the form of linear matrix inequalities (LMIs) to guarantee the stochastic stability of the closed-loop system and the prescribed $L_2-L_{\infty }$ performance. The effectiveness and superiority of the proposed methodology are verified through extensive numerical simulations on two practical case studies, namely, a bio-economic system and a DC motor system. In the case of actuator faults and data dropouts the observer achieves accurate state tracking, and the peak value of the system output is strictly constrained. The research results confirm that the method has strong robustness against data dropouts and actuator faults. Full article

Reliable fault detection in brushless DC motors is challenging owing to environmental complexity and high equipment costs. To address these challenges, we propose an effective and cost-effective approach using an optimized end-to-end one-dimensional convolutional neural network. Specifically, a real experimental platform simulating bearing and eccentricity faults was developed. Statistical t-tests indicated that three-axis accelerometer signals from a low-cost inertial measurement unit provided sufficient fault information for the present diagnosis task. Unlike traditional methods such as support vector machines, multilayer neural networks, and random forests, which rely on manual feature extraction, our model learns directly from raw waveforms and can handle signal drift. Under the present controlled experimental setting and the leave-one-day-out evaluation protocol, the model achieved 100.00% average window-level classification accuracy, considerably outperforming traditional methods, the performances of which declined to 67.95–71.37% under environmental shifts. Moreover, with an inference time of only 0.96 ms, 32 times faster than that of random forests, this approach is well suited for real-time embedded monitoring. The proposed method demonstrates strong potential for cost-efficient and robust fault diagnosis under the present experimental setting. Full article

Traditional approaches for identifying bearing defects in metro traction systems often suffer from low diagnostic efficiency and accuracy. To address this, we propose an information fusion approach using the Bat Algorithm-Optimized Variational Mode Decomposition (BA-VMD) and the Self-Attention Convolutional Neural Network (SA-CNN). Vibration and acoustic emission signals are denoised via BA-VMD to optimize decomposition, followed by a diagnosis model utilizing attention-based fusion and SA-CNN to enhance key feature extraction. Experiments on subway traction motor bearings under varying operating conditions demonstrate the method’s efficacy. Results indicate that BA-VMD achieves a signal-to-noise ratio of 6.791, which is 1.595 higher than that of EMD (5.196). Furthermore, the SA-CNN model achieves an average diagnostic accuracy of 98.6%, significantly outperforming MLP (93.57%) and SVM (90.90%). These findings confirm that the proposed framework ensures accurate and stable bearing fault detection in highly variable operating conditions. Full article

This paper proposes a fault-tolerant optimal design method for quad-winding permanent magnet synchronous motors (PMSMs) considering irreversible demagnetization under fault conditions. In quad-winding motors, when one or more winding sets become unavailable, the remaining windings must carry higher current to maintain the required torque. This increases the external magnetomotive force acting on the permanent magnets and may cause irreversible demagnetization, particularly in spoke-type magnet structures. To address this issue, the demagnetization characteristics of the quad-winding motor were analyzed under healthy and faulty operating conditions. Based on this analysis, an optimization process using a Radial Basis Function–Multi-Layer Perceptron (RBF–MLP) surrogate model and a combination of grid-based search and local optimization was applied to obtain an optimal motor design. The optimization results show that the irreversible demagnetization ratio was reduced from 5.9% to 0.5% while maintaining a similar magnet volume. The proposed design approach effectively suppresses irreversible demagnetization in quad-winding PMSMs. Full article

Maintaining safe and consistent performance in industrial energy networks necessitates the dependable detection of asynchronous motor failures. However, in practical scenarios, diagnostic models often suffer from poor generalization and high false alarm rates when faced with incomplete datasets and limited high-quality samples. Aiming to overcome the aforementioned constraints, a PCA-KPLS integrated multi-fidelity scheme is presented in this work. The method utilizes low-fidelity data to construct a Principal Component Analysis (PCA) model for extracting basic features, and then integrates a small amount of high-fidelity target data via Kernel Partial Least Squares (KPLS) to establish a cross-domain feature mapping, enabling knowledge transfer between data of different fidelities. Validation through mathematical simulation and an engineering case study on a primary air fan demonstrates that the proposed method achieves higher prediction accuracy and lower root-mean-square error compared to models using only low-fidelity or high-fidelity data, significantly reduces false alarms, and enhances the accuracy of fault diagnosis and model generalization capability when training samples are insufficient. Full article

To satisfy the stringent functional-safety requirements of steer-by-wire steering systems for advanced autonomous driving, this paper proposes a novel dual-motor collaborative fault-tolerant control strategy. The proposed approach aims to overcome the insufficient fault tolerance of conventional single-motor architectures, as well as the limited dynamic response and disturbance-rejection capability observed in existing multi-motor schemes. The key contribution is an integrated control framework consisting of two components: (i) dual-motor torque synchronization achieved via a fuzzy-PID–based mean-deviation coupling method, and (ii) a super-spiral sliding-mode control law optimized by an adaptive differential-evolution algorithm to enhance the dynamic performance and robustness of the current loop. Experimental results demonstrate that, relative to a non-synchronized baseline, the proposed strategy reduces the inter-motor current mismatch by 8.1–78.6% across multiple operating conditions. Moreover, following fault occurrence, the proposed Self-Adaptive Differential-Evolution-algorithm-based Super-Twisting Sliding-Mode Control method shortens the stabilization time by 50–70%, 9–20%, and 16.7% compared with conventional PID, Super-Twisting Sliding-Mode Control methods, and classical $H_{\infty }$ robust control, respectively. Overall, the developed solution meets functional-safety requirements and provides a highly reliable steering-actuation mechanism for advanced autonomous driving applications. Full article